Deep Learning on Construction Sites: A Case Study of Sparse Data Learning Techniques for Rebar Segmentation.

Abstract

Recent advances in deep learning models for image interpretation finally made it possible to automate construction site monitoring processes that rely on remote sensing. However, the major drawback of these models is their dependency on large datasets of training images labeled at pixel level, which must be produced manually by skilled personnel. To reduce the need for training data, this study evaluates weakly and semi-supervised semantic segmentation models for construction site imagery to efficiently automate monitoring tasks. As a case study, we compare fully, weakly and semi-supervised methods for the detection of rebar covers, which are useful for quality control. In the experiments, recent models, i.e., IRNet, DeepLabv3+ and the cross-consistency training model are compared for their ability to segment rebar covers from construction site imagery with minimal manual input. The results show that weakly and semi-supervised models can indeed rival with the performance of fully supervised models with the majority of the target objects being properly found. This study provides construction site stakeholders with detailed information on how to leverage deep learning for efficient construction site monitoring and weigh preprocessing, training, and testing efforts against each other in order to decide between fully, weakly and semi-supervised training.